Least Squares Support Vector Machine Implementation with MATLAB Functions

This documentation covers the Least Squares Support Vector Machine (LS-SVM) algorithm implementation. The LS_SVM.m function serves as the core algorithm module, implementing the LS-SVM optimization problem using quadratic programming and matrix inversion techniques to solve for support vectors and bias terms. The normalization.m function performs data preprocessing by scaling input features to a standardized range (typically [0,1] or [-1,1]) using min-max normalization, which enhances numerical stability during computation. The release.m function handles data post-processing by reversing the normalization transformation using stored scaling parameters, restoring predictions to their original data scale. These preprocessing and post-processing functions play a critical role in improving algorithm convergence and prediction accuracy by ensuring consistent data scaling across training and testing phases. The implementation follows standard LS-SVM methodology where the solution is obtained through solving a linear system rather than quadratic programming, making it computationally efficient for medium-sized datasets.